Abstract : We propose a new stochastic algorithm for Bayesian optimal design in nonlinear and high dimensional models. Like in the recent work of Peter Müller, we turn the optimization problem into a matter of Monte Carlo Markov chains simulations to explore the expected utility surface. The optimal design is then the mode of this surface seen as a probability distribution. Our algorithm mixes a "particles" method to efficiently explore high dimensional multimodal surfaces, with simulated annealing to concentrate the samples near the modes. We test it in a multiple change-point problem for time series data.